Gastos Casa
08 agosto, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::scroll_box(width = "100%", height = "300px")| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 27/6/2022 | Agua | 12.502 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 29/6/2022 | Netflix | 8.320 | Tami | NA |
| 29/6/2022 | Comida | 68.213 | Tami | NA |
| 30/6/2022 | Comida | 15.310 | Tami | NA |
| 30/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 2/7/2022 | Diosi | 35.990 | Andrés | NA |
| 3/7/2022 | Gas | 19.600 | Andrés | NA |
| 3/7/2022 | Parafina | 44.029 | Tami | NA |
| 11/7/2022 | Diosi | 15.930 | Tami | NA |
| 11/7/2022 | Comida | 60.660 | Tami | NA |
| 14/7/2022 | Enceres | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Comida | 15.000 | Andrés | NA |
| 19/7/2022 | Parafina | 22.521 | Tami | NA |
| 20/7/2022 | VTR | 21.990 | Andrés | NA |
| 21/7/2022 | Comida | 24.660 | Andrés | NA |
| 23/7/2022 | Enceres | 14.315 | Andrés | NA |
| 23/7/2022 | Comida | 22.263 | Andrés | NA |
| 20/7/2022 | Comida | 41.830 | Andrés | NA |
| 25/7/2022 | Comida | 61.470 | Tami | NA |
| 25/7/2022 | Comida | 16.100 | Tami | NA |
| 25/7/2022 | Cortina baño | 29.120 | Tami | NA |
| 28/7/2022 | Electricidad | 78.798 | Andrés | NA |
| 29/7/2022 | Netflix | 8.320 | Tami | NA |
| 30/7/2022 | Comida | 36.170 | Tami | NA |
| 31/7/2022 | Parafina | 22.060 | Tami | NA |
| 1/8/2022 | Comida | 11.670 | Andrés | NA |
| 31/3/2019 | Comida | 9.000 | Andrés | NA |
| 8/9/2019 | Comida | 24.588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.2171e+08 2 4.7112 0.0094 **
## lag_depvar 7.3943e+10 1 1652.1471 <2e-16 ***
## Residuals 2.1170e+10 473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 900.033 13557.64 0.0204546
## 2-0 27931.231 22077.515 33784.95 0.0000000
## 2-1 20702.393 17138.887 24265.90 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 321 50165.49 16170.341
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2067.763067 4061.316190 -558.827025 2420.078705 -3010.673873
## 7 8 9 10 11
## 503.828371 -5677.781757 -1162.933576 -3938.668597 -364.377576
## 12 13 14 15 16
## -4893.759300 -1531.289921 -822.119215 448.604209 -3188.385284
## 17 18 19 20 21
## -306.876653 -2069.220132 6671.194997 -1531.913993 -1202.029265
## 22 23 24 25 26
## 1486.978926 -1193.839087 234.035182 1687.986861 -7127.187532
## 27 28 29 30 31
## 982.227724 8210.218091 359.116475 -73.553887 -2456.853164
## 32 33 34 35 36
## 1543.296312 4525.869375 1042.570786 2303.741609 -1969.492811
## 37 38 39 40 41
## 4530.955767 4258.359671 -2352.379974 -3029.233123 -1126.655279
## 42 43 44 45 46
## -10746.701914 7376.653723 2570.537588 1356.111779 8083.678121
## 47 48 49 50 51
## 597.403387 6444.964540 6584.750118 -6053.634797 -4894.938032
## 52 53 54 55 56
## -5105.821791 -7925.723123 6200.057781 -4068.256033 -4852.374933
## 57 58 59 60 61
## 3934.134699 922.764005 -9.604814 161.790290 -4980.936788
## 62 63 64 65 66
## 18182.873023 3533.583313 -3771.288334 5846.995260 7224.378356
## 67 68 69 70 71
## 14470.984114 1420.521497 -13465.750489 -1413.894383 4560.344646
## 72 73 74 75 76
## -5013.129872 -4461.260709 -10509.361703 2546.669046 -5351.389182
## 77 78 79 80 81
## 1152.364035 -6798.215565 665.342811 -2256.027149 -2587.679488
## 82 83 84 85 86
## -3816.082536 -402.646451 2436.865029 3849.231575 519.691760
## 87 88 89 90 91
## -451.680285 229.106819 4328.239004 -1177.536743 1147.813521
## 92 93 94 95 96
## -2077.425139 -1038.165019 191.589013 285.127361 -7477.733579
## 97 98 99 100 101
## 2461.603572 -8562.400034 -2831.379399 -3918.729498 -1596.374269
## 102 103 104 105 106
## -1123.752584 3311.969406 -2255.324495 2689.778777 -1097.474003
## 107 108 109 110 111
## 1033.406806 2633.377602 -3136.660843 -4680.605817 -772.608423
## 112 113 114 115 116
## 1978.452979 11742.255633 -1301.506415 2627.461925 4203.574202
## 117 118 119 120 121
## 3413.748951 -1208.202537 -4801.681560 -3758.148403 2321.969738
## 122 123 124 125 126
## -1751.046471 1339.071716 8845.207484 758.846928 45.735956
## 127 128 129 130 131
## -2596.687709 2610.690924 6990.515426 897.008024 -8609.307059
## 132 133 134 135 136
## 1726.348213 4100.113256 -3230.952053 -1451.228271 -869.525538
## 137 138 139 140 141
## -3886.506469 1210.629390 -481.899738 -2897.756132 1756.907940
## 142 143 144 145 146
## -1862.395805 -7797.022354 2135.068316 -3414.138714 2189.274244
## 147 148 149 150 151
## -199.977187 1075.113226 -323.023964 1386.619385 1204.634672
## 152 153 154 155 156
## 3361.664574 -4886.752723 -1154.578432 -3208.613028 6008.133037
## 157 158 159 160 161
## 9739.678968 -3122.532532 -4452.218330 3956.490356 497.596934
## 162 163 164 165 166
## 2984.992837 -5655.929886 -6447.755116 4503.202247 17684.372438
## 167 168 169 170 171
## 3748.728769 -301.318815 -2333.005660 -958.858061 3751.885339
## 172 173 174 175 176
## -95.309946 -7932.914266 3085.475274 4517.552077 778.306825
## 177 178 179 180 181
## 8902.319554 -9171.273646 -3296.295873 -10535.778505 -10936.146179
## 182 183 184 185 186
## 1626.521461 9648.311021 -1180.583749 6183.217043 6746.369811
## 187 188 189 190 191
## 13286.562798 8441.389779 -4113.029027 2477.807511 10375.978992
## 192 193 194 195 196
## -1715.489965 -2473.128834 -10263.296611 -6231.656027 1429.319182
## 197 198 199 200 201
## -5052.311897 -9566.807101 5698.562719 -2820.631815 -1444.440480
## 202 203 204 205 206
## -531.155988 6762.924565 10075.562372 671.989300 3020.577255
## 207 208 209 210 211
## 3173.668617 5840.902209 12847.760731 -5777.986636 -11300.615531
## 212 213 214 215 216
## -5541.049371 -10401.778023 -4789.175250 1848.310413 -12721.141276
## 217 218 219 220 221
## 16790.109306 8024.364787 1661.598700 26810.673544 12393.578910
## 222 223 224 225 226
## 7115.541091 13781.954021 -4245.262721 -1974.343904 3609.651684
## 227 228 229 230 231
## 197.110557 2621.328376 8890.927061 5667.485729 -2080.508334
## 232 233 234 235 236
## -1939.276277 9368.288341 -11627.382328 -7255.491241 -8425.350213
## 237 238 239 240 241
## -9896.619678 3375.628055 1605.407810 -8066.347366 -8685.626194
## 242 243 244 245 246
## 9469.607003 -7503.064234 2813.794112 -10017.615750 -3684.307066
## 247 248 249 250 251
## 1807.267329 1347.410072 -12002.891318 4058.259668 2411.430901
## 252 253 254 255 256
## 4517.952136 2382.106283 -944.687413 11359.605058 20983.419134
## 257 258 259 260 261
## 3096.708047 -4373.892962 4079.701131 -1743.331388 3729.707900
## 262 263 264 265 266
## -4876.841607 -10848.667632 -4561.879261 -309.154312 -4977.404799
## 267 268 269 270 271
## 9033.714569 -4126.168240 4385.478601 -1959.392016 4599.173151
## 272 273 274 275 276
## 829.336687 7418.597450 -1368.122613 12094.538920 -4631.147672
## 277 278 279 280 281
## 1746.225961 -355.797706 7885.909330 -5092.183158 -2692.384269
## 282 283 284 285 286
## -11181.039100 -2462.458488 18883.234773 7806.301439 2688.720322
## 287 288 289 290 291
## -681.040101 882.897945 6384.807504 6819.323059 -18884.369649
## 292 293 294 295 296
## -11018.203119 -7875.365123 9989.612239 3274.323168 -1014.739602
## 297 298 299 300 301
## 27578.461079 9942.053949 4700.501663 9306.056683 2584.570561
## 302 303 304 305 306
## -1284.688637 7704.148669 -24531.922648 -3455.564814 -45.244354
## 307 308 309 310 311
## -6830.334321 -3751.794255 3191.990776 -8973.709371 -2913.038212
## 312 313 314 315 316
## -7847.709342 1978.340445 -2781.833902 2431.149102 -3746.568347
## 317 318 319 320 321
## 27807.927678 -711.935943 3327.424964 10843.040336 5496.424810
## 322 323 324 325 326
## 32254.525550 4656.528188 -21372.633543 1666.888268 1000.838854
## 327 328 329 330 331
## -6552.917223 -1717.052347 -33211.148243 1367.473317 -1854.463212
## 332 333 334 335 336
## 357.251967 -2740.858187 4526.728694 -67.677446 -6594.512933
## 337 338 339 340 341
## -2693.011034 -1754.435118 -7240.961996 4355.002448 -945.614707
## 342 343 344 345 346
## -1319.916352 -578.424467 581.283568 862.889048 -1261.938107
## 347 348 349 350 351
## -9087.641216 -12757.888218 2888.603195 -3814.343594 -3133.038779
## 352 353 354 355 356
## -5447.488383 2316.486085 1887.664737 3204.855138 -3377.362120
## 357 358 359 360 361
## -105.200068 1069.674726 7375.148043 534.852538 209.513185
## 362 363 364 365 366
## 2825.507268 -2543.610626 -637.251420 -8495.566980 -4274.830654
## 367 368 369 370 371
## -5819.472760 -4500.344488 -6769.253847 5555.980291 811.814526
## 372 373 374 375 376
## 7528.556824 -7338.331746 -1881.391944 -2997.354859 -2054.610205
## 377 378 379 380 381
## -12036.910497 2452.202601 -10146.912170 6281.374995 9807.121078
## 382 383 384 385 386
## 3450.301246 -2128.058506 1896.169671 7004.751963 11579.765337
## 387 388 389 390 391
## -5773.824532 -5248.488125 29.126817 8751.048144 1895.367842
## 392 393 394 395 396
## 11290.224832 -9938.273000 2860.870588 774.433520 627.953677
## 397 398 399 400 401
## -582.786482 -471.968920 -14379.190712 8825.830801 -991.691293
## 402 403 404 405 406
## -1165.343362 7206.974809 -7796.771730 -1051.729699 -2271.425185
## 407 408 409 410 411
## -5529.597971 -2501.295734 -3536.500106 -8341.466807 6637.232310
## 412 413 414 415 416
## 2034.076252 -7021.135857 -7261.922058 14724.690249 4102.636276
## 417 418 419 420 421
## 4716.732434 -7873.802776 -4475.063400 -2276.539845 3166.718598
## 422 423 424 425 426
## -13713.946051 -2324.783903 -8623.652548 3575.338944 7461.692240
## 427 428 429 430 431
## 6939.031145 -3726.367354 -3814.490259 -4373.235570 -1395.006727
## 432 433 434 435 436
## -5314.195808 -6176.740082 -5442.103250 -843.598286 -318.649840
## 437 438 439 440 441
## -4471.211181 3115.274176 5303.389690 -4683.402027 -1739.145305
## 442 443 444 445 446
## 1999.576901 -3457.568606 3245.106581 -6226.460631 -11690.933423
## 447 448 449 450 451
## -3962.418677 10213.283754 -1624.290141 5166.854989 -5537.815441
## 452 453 454 455 456
## -729.054782 772.855396 3392.353458 -11955.720998 3824.957858
## 457 458 459 460 461
## -6313.336495 6975.204457 3358.477571 2802.450655 -3588.903473
## 462 463 464 465 466
## 2394.708764 260.705492 2057.111353 -282.934256 3594.601349
## 467 468 469 470 471
## -2437.831216 6043.780778 -6775.057335 -2701.062519 -1906.129434
## 472 473 474 475 476
## -4342.247422 3367.922888 8117.381972 -5802.282584 1777.614337
## 477 478
## -5908.754067 -2500.015573
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17201.52 20077.68 24374.97 24090.06 26467.39 23772.89 24496.50 19680.08
## 10 11 12 13 14 15 16 17
## 19413.95 16729.66 17515.05 14211.15 14262.83 14934.25 16648.10 14951.02
## 18 19 20 21 22 23 24 25
## 15996.22 15363.38 22517.91 21592.60 21067.16 22976.41 22295.54 22954.73
## 26 27 28 29 30 31 32 33
## 24819.47 18686.06 20429.78 28346.88 28405.13 28074.71 25679.99 27096.70
## 34 35 36 37 38 39 40 41
## 30978.86 31330.83 32754.35 30239.62 34184.64 37425.38 34451.52 31229.94
## 42 43 44 45 46 47 48 49
## 30065.99 20549.63 28144.89 30606.17 31706.46 38614.17 38103.61 42813.25
## 50 51 52 53 54 55 56 57
## 47092.63 39716.22 34229.39 29201.44 22276.09 28630.11 25175.95 21435.87
## 58 59 60 61 62 63 64 65
## 25889.09 27161.46 27461.50 27877.51 23706.41 40466.56 42329.29 37526.86
## 66 67 68 69 70 71 72 73
## 41776.62 46742.30 57519.05 55512.61 40605.61 38086.08 41134.70 35376.83
## 74 75 76 77 78 79 80 81
## 30782.79 21391.62 24625.67 20509.92 22617.22 17460.80 19496.74 18715.39
## 82 83 84 85 86 87 88 89
## 17733.23 15782.50 17073.28 20718.05 25180.74 26180.68 26205.89 26828.90
## 90 91 92 93 94 95 96 97
## 30995.97 29814.62 30824.14 28868.88 28060.55 28432.44 28843.16 22355.25
## 98 99 100 101 102 103 104 105
## 25400.97 18360.52 17205.02 15225.80 15528.61 16212.89 20731.04 19805.22
## 106 107 108 109 110 111 112 113
## 23352.05 23139.88 24833.05 27739.09 25211.75 21619.04 21897.26 24570.46
## 114 115 116 117 118 119 120 121
## 35545.51 33719.97 35576.14 38604.97 40580.77 38245.68 33014.01 29318.17
## 122 123 124 125 126 127 128 129
## 31422.19 29684.64 30878.22 38555.30 38194.12 37246.12 34077.74 35877.06
## 130 131 132 133 134 135 136 137
## 41329.85 40764.45 31876.65 33154.32 36376.52 32750.66 31121.53 30197.22
## 138 139 140 141 142 143 144 145
## 26719.23 28148.04 27915.33 25578.09 27623.11 26233.88 19770.93 22832.28
## 146 147 148 149 150 151 152 153
## 20636.87 23644.26 24189.74 25796.31 25980.24 27651.22 28965.19 32028.18
## 154 155 156 157 158 159 160 161
## 27452.29 26707.76 24238.15 30192.18 41142.96 39456.22 36794.37 41865.69
## 162 163 164 165 166 167 168 169
## 43288.58 46739.22 42159.04 37418.51 42898.91 59366.84 61601.46 59999.43
## 170 171 172 173 174 175 176 177
## 56792.86 55175.83 57905.88 56920.06 49133.81 51986.02 55766.69 55803.25
## 178 179 180 181 182 183 184 185
## 63004.56 53410.30 50128.21 40843.43 32296.76 35840.69 46046.87 45497.35
## 186 187 188 189 190 191 192 193
## 51510.63 57314.01 68206.61 73543.17 67173.76 67369.16 74511.35 70143.84
## 194 195 196 197 198 199 200 201
## 65621.15 54755.66 48725.11 50163.88 45713.81 37803.01 44293.06 42502.44
## 202 203 204 205 206 207 208 209
## 42136.73 42619.93 49483.01 58462.58 58088.42 59830.76 61503.38 65333.10
## 210 211 212 213 214 215 216 217
## 74895.84 66898.19 54967.19 49521.21 40426.03 37352.83 40498.14 30416.89
## 218 219 220 221 222 223 224 225
## 47562.92 54958.12 55869.18 78865.99 86437.17 88460.76 96129.26 86988.20
## 226 227 228 229 230 231 232 233
## 80925.63 80503.32 77119.24 76272.22 81057.37 82435.51 76814.42 71978.71
## 234 235 236 237 238 239 240 241
## 77689.81 64201.92 56157.49 48026.33 39552.66 43787.16 45961.78 39345.91
## 242 243 244 245 246 247 248 249
## 32961.25 43348.21 37536.63 41512.33 33697.59 32390.30 36082.73 38935.32
## 250 251 252 253 254 255 256 257
## 29671.60 35670.00 39510.05 44757.61 47503.54 46990.97 57396.58 75071.58
## 258 259 260 261 262 263 264 265
## 74884.75 68127.44 69624.33 65806.72 67267.56 60961.81 50127.45 46114.44
## 266 267 268 269 270 271 272 273
## 46325.98 42393.14 51286.74 47521.95 51710.82 49808.26 53916.95 54215.97
## 274 275 276 277 278 279 280 281
## 60294.55 57904.75 67676.00 61539.06 61751.23 60083.52 65884.75 59551.53
## 282 283 284 285 286 287 288 289
## 56080.47 45526.60 43907.05 61314.41 66900.71 67314.33 64705.67 63783.76
## 290 291 292 293 294 295 296 297
## 67825.39 71775.37 52578.77 42580.22 36530.39 46956.68 50231.45 49336.40
## 298 299 300 301 302 303 304 305
## 73778.66 79784.50 80458.94 85118.29 83298.55 78278.28 81780.35 56423.99
## 306 307 308 309 310 311 312 313
## 52647.10 52323.62 46050.65 43231.72 46871.71 39348.18 38057.28 32563.52
## 314 315 316 317 318 319 320 321
## 36386.55 35559.57 39430.00 37393.93 63442.51 61261.72 62901.82 70981.29
## 322 323 324 325 326 327 328 329
## 73392.90 99133.76 97494.92 73079.25 71864.88 70205.49 62075.34 59168.29
## 330 331 332 333 334 335 336 337
## 28810.96 32536.03 32980.03 35323.57 34657.70 40483.39 41569.94 36769.15
## 338 339 340 341 342 343 344 345
## 35975.58 36103.53 31374.85 37434.90 38105.06 38366.14 39250.86 41054.97
## 346 347 348 349 350 351 352 353
## 42895.51 42644.64 35517.46 25989.25 31388.34 30237.75 29823.63 27415.80
## 354 355 356 357 358 359 360 361
## 32142.34 35934.86 40443.93 38614.49 39887.61 42047.85 49518.43 50074.63
## 362 363 364 365 366 367 368 369
## 50278.35 52766.61 50224.39 49663.28 42233.54 39401.76 35539.77 33295.83
## 370 371 372 373 374 375 376 377
## 29313.45 36675.61 38985.87 46951.76 40861.96 40303.50 38825.90 38353.91
## 378 379 380 381 382 383 384 385
## 29128.51 33773.48 26754.34 35057.45 45495.84 49097.63 47353.40 49365.39
## 386 387 388 389 390 391 392 393
## 55648.95 65231.11 58373.20 52785.02 52510.95 59965.78 60494.49 69251.56
## 394 395 396 397 398 399 400 401
## 58246.13 59829.00 59384.62 58863.22 57334.68 56083.62 42707.17 51380.41
## 402 403 404 405 406 407 408 409
## 50370.63 49326.31 55792.91 48259.30 47563.43 45873.03 41506.15 40324.93
## 410 411 412 413 414 415 416 417
## 38369.04 32402.91 40356.07 43312.28 37930.21 32968.31 47991.79 51875.84
## 418 419 420 421 422 423 424 425
## 55845.23 48237.49 44523.25 43185.71 46808.80 35109.64 34836.08 29036.23
## 426 427 428 429 430 431 432 433
## 34683.16 43095.83 50058.37 46790.78 43829.52 40723.29 40610.34 37052.17
## 434 435 436 437 438 439 440 441
## 33151.10 30356.88 31949.08 33817.35 31801.58 36717.47 42986.40 39705.57
## 442 443 444 445 446 447 448 449
## 39408.57 42445.71 40310.18 44340.46 39538.79 30479.42 29305.00 40778.00
## 450 451 452 453 454 455 456 457
## 40456.29 46165.24 41756.77 42110.00 43747.08 47503.29 37274.04 42172.91
## 458 459 460 461 462 463 464 465
## 37549.37 45195.81 48751.84 51399.19 48095.29 50460.01 50663.60 52428.51
## 466 467 468 469 470 471 472 473
## 51920.97 54894.83 52195.79 57298.63 50489.63 48076.13 46647.82 43237.65
## 474 475 476 477 478
## 47032.19 54571.85 48941.81 50662.47 45398.02
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8571
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.71117 0.5771798 2.954044
## t2* 1652.14712 29.1839457 242.778390
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.309763 4.846732 10.87218
## 2 lag_depvar 1314.772242 1666.937500 2099.63928Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 08 00:53:01 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 08 00:53:09 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 08 00:53:17 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 08 00:53:25 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 08 00:53:34 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 08 00:53:42 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 08 00:53:50 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 08 00:53:59 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 08 00:54:07 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 08 00:54:15 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3) %>%
knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
col.names= c("Item","2023","2022","2021","2020")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 6.281714 | 6.008526 | 7.529516 |
| Comida | NA | 300.780286 | 311.590579 | 343.078226 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 43.067143 | 34.512947 | 29.129064 |
| Enceres | NA | 14.113571 | 14.547842 | 24.017839 |
| Farmacia | NA | 3.140000 | 9.996474 | 11.560452 |
| Gas/Bencina | NA | 61.750000 | 31.329158 | 25.882387 |
| Diosi | NA | 19.003857 | 40.277947 | 39.056032 |
| donaciones/regalos | NA | 0.000000 | 9.056947 | 8.861903 |
| Electrodomésticos/ Mantención casa | NA | 6.761143 | 38.235158 | 26.757032 |
| VTR | NA | 27.990000 | 22.367000 | 21.107677 |
| Netflix | NA | 7.505286 | 7.204316 | 7.608032 |
| Otros | NA | 5.401857 | 1.990158 | 1.219774 |
| Total | 0 | 495.794857 | 527.117053 | 545.807935 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1682, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-08-09 00:04:58 sería de: 34.530 pesos// Percentil 95% más alto proyectado: 37.582,14
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 33599.83 | 33568.79 |
| Lo.80 | 33689.29 | 33661.54 |
| Point.Forecast | 34529.69 | 36388.94 |
| Hi.80 | 36218.05 | 41084.90 |
| Hi.95 | 37144.98 | 43570.79 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3194 989.7393
## s.e. 0.1523 37.5655
##
## sigma^2 = 29175: log likelihood = -274.53
## AIC=555.05 AICc=555.69 BIC=560.27paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3472 33.7638
## s.e. 0.1469 1.2768
##
## sigma^2 = 27005: log likelihood = -272.91
## AIC=551.83 AICc=552.46 BIC=557.04forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 910.8519 | 636.4574 | 683.4227 |
| Lo.80 | 1029.7332 | 758.7406 | 765.8957 |
| Point.Forecast | 1254.3052 | 989.7390 | 949.8170 |
| Hi.80 | 1478.8772 | 1220.7375 | 1250.1955 |
| Hi.95 | 1597.7585 | 1343.0207 | 1445.9443 |
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.0
## [7] ggiraph_0.8.2 tidytext_0.3.3 DT_0.23
## [10] autoplotly_0.1.4 rvest_1.0.2 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.17.0 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.1 forcats_0.5.1
## [22] dplyr_1.0.9 purrr_0.3.4 tidyr_1.2.0
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.11 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.7 httr_1.4.3
## [34] readxl_1.4.0 zoo_1.8-10 stringr_1.4.0
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.7
## [43] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.39 uuid_1.1-0 rstudioapi_0.13
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.8.0
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.5.0 fBasics_3042.89.2 rprojroot_2.0.3
## [25] vctrs_0.4.1 generics_0.1.3 xfun_0.31
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.5.7
## [34] nnet_7.3-16 googlesheets4_1.0.0 gtable_0.3.0
## [37] spatial_7.3-14 timeDate_4021.104 rlang_1.0.4
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.0 selectr_0.4-2
## [46] broom_1.0.0 yaml_2.3.5 abind_1.4-5
## [49] modelr_0.1.8 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.1 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 kableExtra_1.3.4
## [58] jquerylib_0.1.4 Rcpp_1.0.9 base64enc_0.1-3
## [61] fracdiff_1.5-1 haven_2.5.0 fs_1.5.2
## [64] magrittr_2.0.3 timeSeries_4021.104 lmtest_0.9-40
## [67] reprex_2.0.1 googledrive_2.0.0 mvtnorm_1.1-3
## [70] sjmisc_2.8.9 hms_1.1.1 evaluate_0.15
## [73] xtable_1.8-4 sjstats_0.18.1 ggeffects_1.1.3
## [76] compiler_4.1.2 KernSmooth_2.23-20 crayon_1.5.1
## [79] minqa_1.2.4 htmltools_0.5.3 tzdb_0.3.0
## [82] lubridate_1.8.0 DBI_1.1.3 sjlabelled_1.2.0
## [85] dbplyr_2.2.1 boot_1.3-28 Matrix_1.3-4
## [88] car_3.1-0 cli_3.3.0 quadprog_1.5-8
## [91] parallel_4.1.2 insight_0.18.0 igraph_1.3.4
## [94] pkgconfig_2.0.3 xml2_1.3.3 svglite_2.1.0
## [97] bslib_0.4.0 webshot_0.5.3 estimability_1.4.1
## [100] anytime_0.3.9 snakecase_0.11.0 janeaustenr_0.1.5
## [103] digest_0.6.29 parameters_0.18.1 janitor_2.1.0
## [106] rmarkdown_2.14 cellranger_1.1.0 curl_4.3.2
## [109] gtools_3.9.3 urca_1.3-0 nloptr_2.0.3
## [112] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
## [115] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.0
## [118] fansi_1.0.3 pillar_1.8.0 fastmap_1.1.0
## [121] glue_1.6.2 bayestestR_0.12.1 bit_4.0.4
## [124] sass_0.4.2 performance_0.9.1 r2d3_0.2.6
## [127] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))